Friday, 9 December 2016
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Wednesday, 9 November 2016
Call For Paper
Bentham Science Publishers would like to invite you to submit your research paper for publishing in the Journal of
Thursday, 3 November 2016
Highlighted Article: Shear-Wave and Strain Elastography: A Comparative Review on Principles, Basic Techniques and Applications.
Shear-Wave and Strain Elastography: A Comparative Review on Principles, Basic Techniques and Applications.
Author(s):
Mahdi Al-Qahtani Pages 269 - 278 ( 10 )
Abstract:
Elastography is relatively a new diagnostic modality that is being used in the field of medicine. There are 2 basic types of elastography techniques; strain elastography and shear-wave elastography. This review details the principle, its applications and draws differences between the two imaging modalities. Literature from PubMed was searched within the year 2015 only, that contain the search terms ‘strain elastography’ and ‘shear wave elastography’ individually. Articles were carefully selected that must cover at least one application of Liver, Breast, Thyroid, Gastrointestinal tract, Prostate and Musculoskeletal. This review opens a new insight into comparative studies for strain elastography and shear wave elastography techniques, as limited data is available as how these two imaging diagnostic modalities behave under same circumstances.
Keywords:
Elastography applications, elastography, shear-wave, strain, ultrasound.
Affiliation:
Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, KSA.
Graphical Abstract:
For More Information Please Visit Our Website Current Medical Imaging Reviews
Wednesday, 26 October 2016
Most Accessed Article: An Effective Approach of CT Lung Segmentation Using Possibilistic Fuzzy C-Means Algorithm and Classification of Lung Cancer Cells with the Aid of Soft Computing Techniques
An Effective Approach
of CT Lung Segmentation Using Possibilistic Fuzzy C-Means Algorithm and
Classification of Lung Cancer Cells with the Aid of Soft Computing Techniques
Author(s):
Tharcis Paulraj and Kezi S. V. ChellliahPages
225-232 (8)
Abstract:
Lung cancer is one of the most common lethal type of diseases. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung cells from the Computed Tomography (CT) images result. Segmentation and classification of lung CT image, based on soft computing, is still a challenging task in the medical field, due to more computational time and accuracy. This paper deals with an improvement in lung cancer detection using Possibilistic Fuzzy C-Means (PFCM) based segmentation. This work also focuses on the normal and abnormal cancer cells that is classified by using the algorithms of SVM (Support Vector Machine), Gaussian Interval Type II Fuzzy Logic System and Genetic Algorithm (SVMFLGA). The results demonstrate that the SVMFLGA outperforms the Gaussian Interval type II fuzzy logic system (GAIT2FLS) in terms of classification accuracy.
Keywords:
Adaptive Network Fuzzy Inference System
(ANFIS), Fuzzy Possibilistic C-Means Algorithm (FPCM), Gray Level Co-Occurrence
Matrix (GLCM), Non-Small Cell Lung Carcinoma (NSCLC), Small Cell Lung Carcinoma
(SCLC), Type II Fuzzy Logic System (T2FLS).
Affiliation:
Department of Electronics and Communication
Engineering, Christian College of Engineering & Technology, Oddanchatram,
Dindigul District, Tamilnadu, India.
